Method and apparatus for recognizing fingerprint

ABSTRACT

A fingerprint recognition method includes receiving an input partial image corresponding to a partial image of a fingerprint of a first user; partitioning the input partial image into a plurality of blocks; performing a comparison operation based on the plurality of blocks and the enrolled partial images corresponding to partial images of an enrolled fingerprint; and recognizing the fingerprint of the first user based on a result of the comparison operation.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of and claims priority under 35U.S.C. §§ 120/121 to U.S. patent application Ser. No. 15/088,545, filedon Apr. 1, 2016, which claims priority under 35 U.S.C. § 119 to KoreanPatent Application No. 10-2015-0053315, filed on Apr. 15, 2015 andKorean Patent Application No. 10-2015-0138515, filed on Oct. 1, 2015, inthe Korean Intellectual Property Office, the entire contents of each ofwhich are incorporated herein by reference in their entirety.

BACKGROUND 1. Field

At least one example embodiment relates to a method and an apparatus forrecognizing a fingerprint.

2. Description of the Related Art

Biometrics-based authentication technology relates to userauthentication using a fingerprint, an iris, a voice, a face, bloodvessels, and the like which are individually unique to a user. Suchbiological characteristics used for the authentication differ fromindividual to individual, rarely change during a lifetime, and have alow risk of being stolen or copied. In addition, individuals do not needto intentionally carry such characteristics, and thus may not experiencean inconvenience using the biological characteristics. In such anauthentication technology, a fingerprint recognition method is verycommonly used for various reasons, for example, a high level ofconvenience, security, and economic efficiency. The fingerprintrecognition method may reinforce security of a user device and readilyprovide various application services such as mobile payment.

SUMMARY

Some example embodiments relate to a fingerprint recognition method.

In some example embodiments, the method may include receiving an inputpartial image corresponding to a partial image of a fingerprint of auser, partitioning the input partial image into blocks, comparing theblocks to enrolled partial images corresponding to partial images of anenrolled fingerprint, and recognizing the fingerprint of the user basedon a result of the comparing.

The receiving may include sensing a partial region of the fingerprint ofthe user through a sensing region smaller than a size of the fingerprintof the user. The enrolled partial images may be generated by iterativelysensing partial regions of a fingerprint of an enrolled user through asensing region smaller than a size of the fingerprint of the enrolleduser.

The comparing may include calculating scores indicating a degree ofmatching between each of the blocks and the enrolled partial images.

The comparing may include matching the blocks to the enrolled partialimages, and comparing the blocks to the enrolled partial images based ona result of the matching. The matching may include determining at leastone of translation information, rotation information, and scaleinformation between the blocks and the enrolled partial images based ona frequency-based matching method.

The comparing may include determining optimal rotation angles withrespect to the enrolled partial images by matching each of the blocks tothe enrolled partial images, rotating the blocks in response to theenrolled partial images based on the optimal rotation angles withrespect to the enrolled partial images, and comparing the blocks rotatedin response to the enrolled partial images to the enrolled partialimages.

The determining of the optimal rotation angles may include determiningrotation angles between the blocks and the enrolled partial images basedon the frequency-based matching method, and determining the optimalrotation angles with respect to the enrolled partial images using scoresbased on the rotation angles.

The comparing may include calculating scores by matching each of theblocks to the enrolled partial images, selecting a predetermined numberof enrolled partial images from among the enrolled partial images basedon the calculated scores, determining optimal rotation angles withrespect to the selected enrolled partial images based on the calculatedscores, rotating the blocks in response to the selected enrolled partialimages based on the optimal rotation angles with respect to the selectedenrolled partial images, and comparing the blocks rotated in response tothe selected enrolled partial images to the selected enrolled partialimages. The predetermined number may be less than a number of theenrolled partial images.

The recognizing may include at least one of authenticating the userbased on the result of the comparing and identifying the user based onthe result of the comparing.

The recognizing may include selecting a predetermined number of pairs ofthe blocks and the enrolled partial images based on the result of thecomparing, and recognizing the fingerprint of the user based on theselected pairs.

The selecting may include selecting the number of pairs based on a scorebetween a block and an enrolled partial image included in each of thepairs.

Other example embodiments relate to a fingerprint recognition apparatus.

In some example embodiments, the apparatus may include a fingerprintsensor configured to receive an input image corresponding to afingerprint of a user, and at least one processor configured topartition the input image into blocks, compare the blocks to at leastone enrolled image corresponding to an enrolled fingerprint, andrecognize the fingerprint of the user based on a result of thecomparing.

Other example embodiments relate to a bioimage recognition method.

In some example embodiments, the method may include receiving an inputimage corresponding to biodata of a user, partitioning the input imageinto blocks, comparing the blocks to an enrolled image corresponding toenrolled biodata, and recognizing the user based on a result of thecomparing.

Additional aspects of example embodiments will be set forth in part inthe description which follows and, in part, will be apparent from thedescription, or may be learned by practice of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other features and advantages of example embodiments ofthe inventive concepts will become more apparent by describing in detailexample embodiments of the inventive concepts with reference to theattached drawings. The accompanying drawings are intended to depictexample embodiments of the inventive concepts and should not beinterpreted to limit the intended scope of the claims. The accompanyingdrawings are not to be considered as drawn to scale unless explicitlynoted.

FIGS. 1 and 2 illustrate examples of a fingerprint image according to atleast one example embodiment;

FIGS. 3 and 4 illustrate a fingerprint recognition method according toat least one example embodiment;

FIG. 5 illustrates a fingerprint recognition method includingpartitioning an input partial image into blocks and comparing the blocksto enrolled partial images according to at least one example embodiment;

FIG. 6 is a flowchart illustrating an example of a fingerprintrecognition method according to at least one example embodiment;

FIG. 7 is a flowchart illustrating an example of a user authenticationmethod according to at least one example embodiment;

FIG. 8 is a diagram illustrating an example of a frequency-basedmatching method according to at least one example embodiment;

FIG. 9 illustrates an example of a log-polar transformation according toat least one example embodiment;

FIG. 10 illustrates an example of an operation of processing an enrolledimage and an input image to calculate a block score according to atleast one example embodiment;

FIG. 11 is a flowchart illustrating another example of a userauthentication method according to at least one example embodiment; and

FIG. 12 is a diagram illustrating an example of an electronic systemaccording to at least one example embodiment.

DETAILED DESCRIPTION

Detailed example embodiments of the inventive concepts are disclosedherein. However, specific structural and functional details disclosedherein are merely representative for purposes of describing exampleembodiments of the inventive concepts. Example embodiments of theinventive concepts may, however, be embodied in many alternate forms andshould not be construed as limited to only the embodiments set forthherein.

Accordingly, while example embodiments of the inventive concepts arecapable of various modifications and alternative forms, embodimentsthereof are shown by way of example in the drawings and will herein bedescribed in detail. It should be understood, however, that there is nointent to limit example embodiments of the inventive concepts to theparticular forms disclosed, but to the contrary, example embodiments ofthe inventive concepts are to cover all modifications, equivalents, andalternatives falling within the scope of example embodiments of theinventive concepts. Like numbers refer to like elements throughout thedescription of the figures.

It will be understood that, although the terms first, second, etc. maybe used herein to describe various elements, these elements should notbe limited by these terms. These terms are only used to distinguish oneelement from another. For example, a first element could be termed asecond element, and, similarly, a second element could be termed a firstelement, without departing from the scope of example embodiments of theinventive concepts. As used herein, the term “and/or” includes any andall combinations of one or more of the associated listed items.

It will be understood that when an element is referred to as being“connected” or “coupled” to another element, it may be directlyconnected or coupled to the other element or intervening elements may bepresent. In contrast, when an element is referred to as being “directlyconnected” or “directly coupled” to another element, there are nointervening elements present. Other words used to describe therelationship between elements should be interpreted in a like fashion(e.g., “between” versus “directly between”, “adjacent” versus “directlyadjacent”, etc.).

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of exampleembodiments of the inventive concepts. As used herein, the singularforms “a”, “an” and “the” are intended to include the plural forms aswell, unless the context clearly indicates otherwise. It will be furtherunderstood that the terms “comprises”, “comprising,”, “includes” and/or“including”, when used herein, specify the presence of stated features,integers, steps, operations, elements, and/or components, but do notpreclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof.

It should also be noted that in some alternative implementations, thefunctions/acts noted may occur out of the order noted in the figures.For example, two figures shown in succession may in fact be executedsubstantially concurrently or may sometimes be executed in the reverseorder, depending upon the functionality/acts involved.

Example embodiments of the inventive concepts are described herein withreference to schematic illustrations of idealized embodiments (andintermediate structures) of the inventive concepts. As such, variationsfrom the shapes of the illustrations as a result, for example, ofmanufacturing techniques and/or tolerances, are to be expected. Thus,example embodiments of the inventive concepts should not be construed aslimited to the particular shapes of regions illustrated herein but areto include deviations in shapes that result, for example, frommanufacturing.

Although corresponding plan views and/or perspective views of somecross-sectional view(s) may not be shown, the cross-sectional view(s) ofdevice structures illustrated herein provide support for a plurality ofdevice structures that extend along two different directions as would beillustrated in a plan view, and/or in three different directions aswould be illustrated in a perspective view. The two different directionsmay or may not be orthogonal to each other. The three differentdirections may include a third direction that may be orthogonal to thetwo different directions. The plurality of device structures may beintegrated in a same electronic device. For example, when a devicestructure (e.g., a memory cell structure or a transistor structure) isillustrated in a cross-sectional view, an electronic device may includea plurality of the device structures (e.g., memory cell structures ortransistor structures), as would be illustrated by a plan view of theelectronic device. The plurality of device structures may be arranged inan array and/or in a two-dimensional pattern.

Example embodiments described herein may be used for recognizing afingerprint of a user. The recognizing of the fingerprint of the usermay include authenticating or identifying the user. The authenticatingof the user may include, for example, determining whether the user is anenrolled user. A result of the authenticating may be output as true orfalse. The identifying of the user may include, for example, determininga user corresponding to the user among a plurality of enrolled users. Aresult of the identifying may be output as, for example, an identity(ID) of the determined enrolled user. When the user does not correspondto any one of the enrolled users, a signal indicating that the user isnot identified may be output.

Example embodiments described herein may be implemented by a product invarious forms example of which include, but are not limited to, apersonal computer (PC), a laptop computer, a tablet computer, asmartphone, a television (TV), a smart home appliance, an intelligentvehicle, a kiosk, and a wearable device. For example, exampleembodiments described herein may be applied to authenticate a user in,for example, a smartphone, a mobile device, and a smart home system. Inaddition, example embodiments described herein may be applied to apayment service provided through user authentication. Further, exampleembodiments described herein may also be applied to an intelligentautomobile system that automatically starts a vehicle through userauthentication. Hereinafter, example embodiments will be described indetail with reference to the accompanying drawings.

FIGS. 1 and 2 illustrate examples of a fingerprint image according to atleast one example embodiment.

Referring to FIG. 1, a fingerprint sensor (not shown) may sense afingerprint 100 of a user. An example of a fingerprint recognitionapparatus including a fingerprint sensor 310 will be discussed ingreater detail below with reference to FIGS. 3 and 12. The fingerprintsensor (e.g., the fingerprint sensor 310) may sense the fingerprint 100through a sensing region. Here, a size of the sensing region of thefingerprint sensor may be smaller than a size of the fingerprint 100.For example, the sensing region of the fingerprint sensor may have arectangular form smaller than the size of the fingerprint 100. In suchan example, the fingerprint sensor may sense a portion of thefingerprint 100 through the sensing region.

The fingerprint sensor may generate a fingerprint image by capturing thesensed portion of the fingerprint 100. When the size of the sensingregion of the fingerprint sensor is smaller than the size of thefingerprint 100, the fingerprint image generated by the fingerprintsensor may correspond to a partial image including the portion of thefingerprint 100. Further, according to at least some exampleembodiments, it is also possible for the sensing region of thefingerprint sensor to be the same size or larger than a size offingerprint 100, such that the fingerprint image generated by thefingerprint sensor may correspond to a complete image of the fingerprint100.

The fingerprint image may be used to enroll or recognize the fingerprint100 of the user. For example, the fingerprint image may be enrolled inan enrollment method. The enrolled fingerprint image may be stored in,for example, memory or prearranged storage. When the size of the sensingregion of the fingerprint sensor is smaller than the size of thefingerprint 100, a plurality of fingerprint images corresponding topartial images of the fingerprint 100 of the user may be enrolled. Forexample, referring to FIG. 1, partial images 110 through 170 may beenrolled. Each of the partial images 110 through 170 may cover a portionof the fingerprint 100, and the partial images 110 through 170 incombination may entirely cover the fingerprint 100. Here, the partialimages 110 through 170 may overlap one another. Hereinafter, a partialimage of an enrolled fingerprint will be referred to as an enrolledpartial image for ease of description.

In addition, an input fingerprint image may be recognized in arecognition method. For example, the recognition method may includeperforming a comparison of the input fingerprint image and an enrolledfingerprint image. A result of authenticating or identifying a user maybe obtained based on whether the input fingerprint image matches theenrolled fingerprint image. Here, when the size of the sensing region ofthe fingerprint sensor is smaller than the size of the fingerprint 100,the input fingerprint image may correspond to a partial image of thefingerprint 100 of the user. Hereinafter, a partial image of afingerprint of a user will be referred to as an input partial image forease of description. Although described hereinafter, example embodimentsprovide a method of recognizing a fingerprint, which will be referred toas a fingerprint recognition method for simplicity, including comparingan input partial image to enrolled partial images.

Although the sensing region of the fingerprint sensor is illustrated asa rectangular form in FIG. 1, various sizes and forms may be applicableto the sensing region. For example, the sensing region may be providedin a circular form as illustrated in FIG. 2. Referring to FIG. 2, in theenrollment method, a plurality of partial images 210 through 295corresponding to a single fingerprint 200 may be enrolled. Therecognition method may include performing a comparison of a fingerprintimage corresponding to a portion of the fingerprint 200 and the enrolledpartial images 210 through 295.

According to at least some example embodiments, a fingerprint sensorused in the enrollment method may differ from a fingerprint sensor usedin the recognition method. For example, a fingerprint sensor having arectangular-shaped sensing region as illustrated in FIG. 1 may be usedin the enrollment method, and a fingerprint sensor having acircular-shaped sensing region as illustrated in FIG. 2 may be used inthe recognition method, or vice-versa. Alternatively, the samefingerprint sensor may be used in both the enrollment method and therecognition method.

FIGS. 3 and 4 illustrate a fingerprint recognition method according toat least one example embodiment. Referring to FIG. 3, a fingerprintrecognition apparatus 300 includes a fingerprint sensor 310. A size of asensing region of the fingerprint sensor 310 may be smaller than a sizeof a fingerprint of a user. The fingerprint recognition apparatus 300may obtain an input partial image 315 through the fingerprint sensor310. The fingerprint recognition apparatus 300 may obtain enrolledpartial images, for example, an enrolled partial image 321, an enrolledpartial image 322, and an enrolled partial image 323, from a database320. According to at least some example embodiments, the database 320may be a prearranged database. The database 320 may be stored in amemory (not shown) included in the fingerprint recognition apparatus300, or in an external device (not shown), for example, a server, thatmay be connected to the fingerprint recognition apparatus 300 in wire orwirelessly, or through a network. According to at least some exampleembodiments, the size and shape of an input partial image may be thesame as the size of enrolled partial images. For example, the inputpartial image 315 may have the same size and shape as enrolled partialimages 321-323 if the input partial images 315 and the enrolled partialimages 321-323 are both captured using the fingerprint sensor 310. Thefingerprint recognition apparatus 300 may recognize the fingerprint ofthe user by comparing the input partial image 315 to the enrolledpartial images 321 through 323. Referring to FIG. 4, the fingerprintrecognition apparatus 300 may match the input partial image 315 to theenrolled partial image 323 to compare the input partial image 315 to theenrolled partial image 323. For example, the fingerprint recognitionapparatus 300 may scale, rotate, and/or translate the input partialimage 315 to overlap a region of the input partial image 315 and aregion of the enrolled partial image 323 which are included in both theinput partial image 315 and the enrolled partial image 323. However,each of the input partial image 315 and the enrolled partial image 323is a partial image and thus, a size of the overlapping region may beconsiderably smaller than a size of each of the input partial image 315and the enrolled partial image 323. For example, overlapping region 325is the region where partial image 315 and partial image 323 overlap. Asis illustrated in FIG. 4, the size of overlapping region 325 may berelatively small in comparison to the full size of partial image 315. Insuch an example, matching the input partial image 315 and the enrolledpartial image 323 may be ineffective.

In addition, the input partial image 315 may include a portion deformedby various factors. For example, a fingerprint image may be deformed bya pressure applied to a sensor. When the input partial image 315 isgenerated, a pressure may be differently applied to each portion of asensing region of a fingerprint sensor. Thus, at least a portion of theinput partial image 315 may be deformed. In addition, the enrolledpartial images 321 through 323 may include a portion deformed by variousfactors. In such a case, comparing the input partial image 315 to theenrolled partial images 321 through 323 may reduce reliability offingerprint recognition.

FIG. 5 illustrates a fingerprint recognition method includingpartitioning an input partial image into blocks and comparing the blocksto enrolled partial images according to at least one example embodiment.Referring to FIG. 5, the fingerprint recognition apparatus 300 of FIG. 3may partition the input partial image 315 into blocks, for example, ablock 511, a block 512, a block 513, and a block 514. The partitioningof the input partial image 315 into the blocks 511 through 514 may alsobe referred to as block partitioning. The fingerprint recognitionapparatus 300 may compare the blocks 511 through 514 to the enrolledpartial images 321 through 323 of FIG. 3, rather than compare the inputpartial image 315 to the enrolled partial images 321 through 323. AsFIG. 5 illustrates, the fingerprint recognition apparatus 300 maypartition an input partial image into blocks such that each of theblocks is smaller, in area, than the input partial image.

The partitioning (or block partitioning) of an image, as discussed inthe present disclosure, may refer to one or both of an operation offorming several different blocks of an image that may overlap each other(as is shown in FIG. 5), and an operation of forming several differentnon-overlapping blocks of the image that may or may not be adjacent toeach other.

The fingerprint recognition apparatus 300 may partition the inputpartial image 315 using various methods. For example, the fingerprintrecognition apparatus 300 may partition the input partial image 315based on a desired or, alternatively, predetermined pattern. The patternmay be determined in advance based on a shape and a size of a sensingregion of a fingerprint sensor, a shape and a size of enrolled partialimages, and the like. As necessary, the pattern may change dynamically.In addition, the partitioning may be performed to allow blocks tooverlap one another, or portions of the blocks to overlap one another.

The fingerprint recognition apparatus 300 may recognize a fingerprintthrough block pattern matching. The block pattern matching may includepattern matching of partial fingerprint images. Although describedhereinafter, the fingerprint recognition apparatus 300 may partition afingerprint image input through the fingerprint sensor into a pluralityof blocks, perform frequency-based matching, arrange matching scores ofthe blocks, and determine whether to authenticate a user using a featurevalue of top K matching scores among the arranged matching scores. Thefeature value may be a value indicating a feature of the top K matchingscores, and include a statistical value, for example, an average. Thefingerprint recognition apparatus 300 may recognize the fingerprintirrespective of a direction of a finger when the fingerprint image issensed.

The fingerprint recognition apparatus 300 may improve efficiency of thematching by using the blocks 511 through 514. As is illustrated in FIG.5, overlapping region 523 is the region where block 513 and partialimage 321 overlap, and overlapping region 522 is the region where block512 and partial image 322 overlap. When the input partial image 315 isinput, an overlapping region between the input partial image 315 and theenrolled partial images 321 through 323 may not be large and thus,performing the matching by partitioning the input partial image 315 intothe blocks 511 through 514 may be effective. For example, thefingerprint recognition apparatus 300 may calculate a rotation angle anda translation of each block with respect to the enrolled partial images321 through 323 based on image frequency information. Here, a proportionof a region included in both the block 513 and the enrolled partialimage 321, hereinafter referred to as an overlapping region, to theblock 513 may be greater than a proportion of the overlapping region tothe input partial image 315. For example, the sizes of overlappingregions 523 and 522 may be relatively large in comparison to the fullsizes of partial images 321 and 322, respectively. Accordingly, a ratioof the size of overlapping region 523 to the size of block 513 may belarger than a ratio of the size of overlapping region 325 to the size ofpartial image 315 in FIG. 4. Similarly, a ratio of the size ofoverlapping region 522 to the size of block 512 may be larger than aratio of the size of overlapping region 325 to the size of partial image315 in FIG. 4. Thus, the matching may be performed more effectively.

In addition, using the blocks 511 through 514, the fingerprintrecognition apparatus 300 may operate robustly against a deformationthat may be included in the input partial image 315 or the enrolledpartial images 321 through 323. For example, the fingerprint recognitionapparatus 300 may use only a block that suitably matches to the enrolledpartial images 321 through 323 among the blocks 511 through 514. Thefingerprint recognition apparatus 300 may exclude a result of acomparison performed using a deformed block, and only use a result of acomparison performed using a non-deformed block. Thus, fingerprintrecognition robust against a deformation may be performed.

In an enrollment method, the fingerprint recognition apparatus 300 maystore only enrolled partial images, and may not store additionalinformation, for example, information about stitching the enrolledpartial images and information about matching the enrolled partialimages. Thus, technology having a low operation complexity andeffectively using a memory may be provided when enrolling the partialimages.

The fingerprint recognition apparatus 300 may match the blocks 511through 514 to the enrolled partial images 311 through 323 using variousmethods. For example, the fingerprint recognition apparatus 300 maydetermine translation information, rotation information, scaleinformation, and various combinations thereof between the blocks 511through 514 and the enrolled partial images 321 through 323 based on afrequency-based matching method. The frequency-based matching method maybe a method of performing matching in a frequency domain.

Translation information between a block and an enrolled partial imagemay include a parameter Tx indicating a translation in an x axis and aparameter Ty indicating a translation in an y axis. Rotation informationbetween a block and an enrolled partial image may include a rotationparameter R. Scale information between a block and an enrolled partialimage may include a scale parameter S. Hereinafter, Tx and Ty will bereferred to as a translation, and R will be referred to as a rotationangle. The fingerprint recognition apparatus 300 may calculate arotation angle, a translation, and a scale parameter by comparing theblocks 511 through 514 to the enrolled partial images 321 through 323 inthe frequency domain. A method of calculating a rotation angle, atranslation, and a scale parameter in a frequency domain will bedescribed in greater detail below with reference to FIG. 8.

The fingerprint recognition apparatus 300 may translate and rotate ablock based on the translation information obtained as a result of thematching. The fingerprint recognition apparatus 300 may scale the blockup or down based on the scale information obtained as the result of thematching. The translation information, the rotation information, and thescale information may be relative information between a block and anenrolled partial image and thus, the fingerprint recognition apparatus300 may translate, rotate, scale up, or scale down the enrolled partialimage in lieu of the block.

When a block and an enrolled partial image overlap due to a translation,rotation, and scaling, the fingerprint recognition apparatus 300 maycalculate a matching score in the overlapping region. For example, thefingerprint recognition apparatus 300 may calculate the matching scorebased on normalized correlation based on an image brightness value. Asdescribed with reference to FIG. 5, the fingerprint recognitionapparatus 300 may accurately perform matching on a rotated inputfingerprint image. Thus, when a fingerprint image is input at an angle,the fingerprint recognition apparatus 300 may accurately recognize afingerprint of the fingerprint image.

FIG. 6 is a flowchart illustrating an example of a fingerprintrecognition method according to at least one example embodiment.Referring to FIG. 6, the fingerprint recognition method includesoperation 610 of receiving an input partial image, operation 620 ofpartitioning the input partial image into blocks, operation 630 ofcomparing the blocks to enrolled partial images, and operation 640 ofrecognizing a fingerprint of a user.

Operation 610 of receiving the input partial image may further includeperforming preprocessing. The preprocessing may include, for example, aseries of operations performed to improve a quality of a fingerprintimage. The fingerprint image may include the input partial image or theenrolled partial images. For example, the preprocessing may includeeliminating noise from the fingerprint image, increasing a contrast ofthe fingerprint image, deblurring the fingerprint image to remove a blurfrom the fingerprint image, and warping performed to correct adistortion included in the fingerprint image.

In addition, the preprocessing may include evaluating the quality of thefingerprint image. For example, when the quality of the fingerprintimage is less than a threshold quality, the preprocessing may includediscarding the obtained fingerprint image and receiving a newfingerprint image. According to at least some example embodiments, thethreshold quality may be set in accordance with the preferences of auser and/or manufacturer of the fingerprint authentication device 300.

Descriptions provided with reference to FIGS. 1 through 5 may beapplicable to the operations described with reference to FIG. 6.Hereinafter, operations 630 and 640 will be further described in detailwith reference to FIGS. 7 and 12.

FIG. 7 is a flowchart illustrating an example of a user authenticationmethod according to at least one example embodiment. Referring to FIG.7, in operation 620, a fingerprint recognition apparatus (e.g., thefingerprint recognition apparatus 300) partitions the input partialimage into N blocks. Here, N is a positive integer greater than or equalto 2. In operation 710, the fingerprint recognition apparatus obtains Lenrolled partial images from a database 715. Here, L is a positiveinteger greater than or equal to 1. In operation 710, the fingerprintrecognition apparatus matches, to the L enrolled partial images, a blockcurrently being processed among the N blocks. In an example, thefingerprint recognition apparatus may match a block to an enrolledpartial image using a frequency-based matching method.

FIG. 8 is a diagram illustrating an example of a frequency-basedmatching method according to at least one example embodiment. Referringto FIG. 8, in operation 1011, information in a time domain, hereinaftersimply referred to as time-domain information, included in a block istransformed to information in a frequency domain, hereinafter simplyreferred to as frequency-domain information, through a fast Fouriertransform (FFT). In operation 1012, an FFT is applied to an enrolledpartial image. Here, the frequency-domain information may be based on anorthogonal coordinates system which expresses information using (x, y)coordinates.

In operation 1021, a coordinates system of frequency-domain informationincluded in the block is transformed to a polar coordinates systemthrough a log-polar transform (LPT). In an example, the LPT may beperformed on magnitude values of pixels in an FFT image obtained throughthe FFT. The polar coordinates system may express information using aradius, an angle, or a combination of a radius and an angle. Inoperation 1022, the LPT is applied to frequency-domain informationincluded in the enrolled partial image.

FIG. 9 illustrates an example of an LPT according to at least oneexample embodiment. Referring to FIG. 9, in an orthogonal coordinatessystem, concentric circles are set based on a central point 1110. Theconcentric circles may be divided into areas based on a radius, anangle, or a combination of a radius and an angle. In an example, the LPTmay map the areas in the orthogonal coordinates system to areas in apolar coordinates system of a radius and an angle. In such an example,the central point 1110 in the orthogonal coordinates system may bemapped to a (0, 0°) area 1115 in the polar coordinates system. Inaddition, an area 1120, an area 1130, an area 1140, and an area 1150 inthe orthogonal coordinates system may be mapped to an area 1125, an area1135, an area 1145, and an area 1155, respectively.

Although not illustrated, the LPT may map areas in the orthogonalcoordinates system to areas in the polar coordinates system of a radius.In such a case, the area 1120 in the orthogonal coordinates system maybe mapped to a (0°) area in the polar coordinates system. The area 1130and the area 1140 in the orthogonal coordinates system may be mapped toa (36°) area in the polar coordinates system. The area 1150 in theorthogonal coordinates system may be mapped to a (324°) area in thepolar coordinates system.

Referring back to FIG. 8, in operation 1031, the FFT is applied to anLPT image of the block. In operation 1032, the FFT is applied to an LPTimage of the enrolled partial image. In operation 1040, a phasecorrelation is performed. A peak is detected as a result of the phasecorrelation, and a location of the detected peak may indicate rotationinformation, for example, θ between the block and the enrolled partialimage.

In another example, the location of the detected peak may indicate scaleinformation between the block and the enrolled partial image. Forexample, one axis of an LPT image corresponds to an angle, and the otheraxis corresponds to a radius. In such an example, a location of a peakdetected through the phase correlation may be expressed as a coordinateof the axis corresponding to an angle and as a coordinate of the axiscorresponding to a radius. The coordinate of the axis corresponding toan angle may indicate the rotation information, and the coordinate ofthe axis corresponding to a radius may indicate the scale information.

In general, a fingerprint image may not have a substantial scale change,and thus a radius may be fixed as a preset value, for example, 1. Insuch a case, a location of a peak detected through the phase correlationmay be expressed as the coordinate of the axis corresponding to anangle. The coordinate of the axis corresponding to an angle may indicatethe rotation information.

In operation 1050, the block is rotated based on the rotationinformation. In operation 1060, the FFT is applied to the rotated block.In operation 1070, the phase correlation is performed. A location of apeak detected as a result of the phase correlation may indicatetranslation information, for example, (Tx, Ty), between the block andthe enrolled partial image. In operation 1080, the rotated block istranslated based on the translation information.

Referring back to FIG. 7, in operation 710, the fingerprint recognitionapparatus calculates L block scores indicating a degree of matching ofthe block currently being processed to the L enrolled partial imagesbased on a result of the matching. As described with reference to FIG.8, the fingerprint recognition apparatus may rotate and translate theblock currently being processed based on a pair of translationinformation and rotation information with respect to a first enrolledpartial image to overlap the block currently being processed and thefirst enrolled partial image.

The fingerprint recognition apparatus may calculate a block score in anoverlapping region. The fingerprint recognition apparatus may calculatea block score using various methods. For example, the fingerprintrecognition apparatus may calculate a block score based on a normalizedcross correlation method based on an image brightness value. Forexample, the fingerprint recognition apparatus may calculate a blockscore based on Equation 1 which defines, for example, a normalized crosscorrelation (ncc) function.

$\begin{matrix}{{{ncc}\left( {I_{1},I_{2}} \right)} = \frac{\sum_{{({i,j})} \in W}{{I_{1}\left( {i,j} \right)}.\mspace{11mu} {I_{2}\left( {{x + i},{y + j}} \right)}}}{\sqrt[2]{\sum_{{({i,j})} \in W}{{I_{1}^{2}\left( {i,j} \right)}.\mspace{11mu} {\sum_{{({i,j})} \in W}{I_{2}^{2}\left( {{x + i},{y + j}} \right)}}}}}} & \left\lbrack {{Equation}\mspace{14mu} 1} \right\rbrack\end{matrix}$

In Equation 1, “W” denotes an overlapping region between an image I₁ andan image I₂. The image I₁ is a rotated block and the image I₂ is anenrolled partial image. The term “i” denotes an X-axis coordinate of apixel in the overlapping region, and the term “j” denotes a Y-axiscoordinate of the pixel in the overlapping region. The term “x” denotestranslation information in an X-axis direction, for example, Tx, and theterm “y” denotes translation information in a Y-axis direction, forexample, Ty. The expression “I₁(i, j)” denotes a pixel value (e.g., apixel brightness value) on (i, j) coordinates of the image I₁. Theexpression “I₂(x+i, y+j)” denotes a pixel value (e.g., a pixelbrightness value) on (x+i, y+j) coordinates of the image I₂.

FIG. 10 illustrates an example of an operation of processing an enrolledimage and an input image to calculate a block score according to atleast one example embodiment. Referring to FIG. 10, an enrolled image1210 is transformed to a first LPT image 1220 through an FFT and an LPT.A block 1215 of an input image is transformed to a second LPT image 1225through an FFT and an LPT.

Rotation information, for example, θ, between the enrolled image 1210and the block 1215 is determined through a phase correlation 1230between the first LPT image 1220 and the second LPT image 1225. Theblock 1215 is rotated based on the determined rotation information.Translation information, for example, (Tx, Ty), between the enrolledimage 1210 and the block 1215 is determined through a phase correlation1250 between an FFT image of the enrolled image 1210 and an FFT image ofthe rotated block 1245.

According to at least some example embodiments, matching of the enrolledimage 1210 and the block 1215 is performed based on the rotationinformation and the translation information. A score 1270 in anoverlapping region between the enrolled image 1210 and the rotated block1245 of the matching image 1260 is calculated. The score 1270 may bealso referred to as a block score and a matching score.

Referring back to FIG. 7, the fingerprint recognition apparatus maycalculate block scores with respect to second through L-th enrolledpartial images. The block score may also be referred to as a matchingscore.

In operation 720, the fingerprint recognition apparatus performs averification operation to determine whether or not a block most recentlyprocessed is a last block among the N blocks. For example, when theprocessed block is not the last block among the N blocks, thefingerprint recognition apparatus may perform operation 710 on a blockyet to be processed. The fingerprint recognition apparatus may calculateN×L block scores by repetitively perform operations 720 and 730 N times.

In operation 730, the fingerprint recognition apparatus selects top Kblock scores from among the N×L block scores. As described withreference to FIG. 4, the input partial image 315 and the enrolledpartial image 323 may partially overlap. In such a case, a block locatedin the overlapping region among the blocks of the input partial image315 may obtain a significant block score, and a block located in anon-overlapping region may obtain an insignificant block score. Thus,the fingerprint recognition apparatus may exclude the insignificantblock score by selecting the top K block scores from among the N×L blockscores. Here, K may be determined within a range less than or equal toN×L.

The fingerprint recognition apparatus may calculate a feature valuebased on the top K block scores. For example, the fingerprintrecognition apparatus may calculate a sum of the top K block scoresusing Equation 2.

Val=Σ_(i−1) ^(k)↓Score(B _(i))  [Equation 2]

In Equation 2, the term “↓Score(B_(i))” denotes an i-th block scorearranged in a descending order. A method of calculating a feature value(Val) may be variously modified, for example, obtaining an average ofthe top K block scores. In addition, different weights may be applied torespective block scores to calculate the feature value based on acalculated desired or, alternatively, optimal rotation angle at which ablock has a highest block score.

In operation 740, the fingerprint recognition apparatus performs userauthentication by comparing the feature value to a threshold value. Forexample, when the feature value is greater than the threshold value, thefingerprint recognition apparatus determines the authentication to besuccessful in operation 750. Conversely, when the feature value is lessthan or equal to the threshold value, the fingerprint recognitionapparatus determines the authentication to be a failure in operation760. The threshold value may be determined based on a method ofcalculating the feature value, the number of enrolled partial images, aquality of the enrolled partial images, and a quality of the inputpartial image.

FIG. 11 is a flowchart illustrating another example of a userauthentication method according to at least one example embodiment.

A fingerprint recognition apparatus (e.g., the fingerprint recognitionapparatus 300) may calculate a rotation angle of each block with respectto enrolled partial images, and calculate desired or, alternatively,optimal rotation angles with respect to the enrolled partial images. Thefingerprint recognition apparatus may identically apply a desired or,alternatively, optimal rotation angle corresponding to each enrolledpartial image to all the blocks, and obtain a matching score bycalculating a translation between a rotated block and an enrolledpartial image.

An operation of the fingerprint recognition apparatus may be dividedinto two phases. In a first phase, the fingerprint recognition apparatusmay match one enrolled partial image to N blocks of an input partialimage (e.g., using the matching method illustrated in FIG. 8) and obtainN matching scores. The fingerprint recognition apparatus may identicallyapply, to all the blocks, a rotation angle of a block having a highestmatching score among the N matching scores obtained as a result of thematching. The block having a highest matching score among the N matchingscores may be, for example, a block having a highest degree ofsimilarity with respect to the enrolled image among the N blocks. In asecond phase, the fingerprint recognition apparatus may calculate atranslation of each block.

Although described in detail hereinafter, the fingerprint recognitionapparatus may select enrolled partial images corresponding to top Mmatching scores based on the matching scores in the first phase, andcalculate a translation only using the selected enrolled partial imagesin the second phase to effectively increase a processing speed.

Referring to FIG. 11, in operation 810, the fingerprint recognitionapparatus obtains L enrolled partial images from a database 815. Inoperation 810, the fingerprint recognition apparatus matches N blocks tothe L enrolled partial images. For example, in operation 810, a matchingoperation may be performed for each enrolled partial image of the Lenrolled partial images. For example, for a first enrolled partial imagefrom among the L enrolled partial images, a fingerprint recognitionapparatus may match the first enrolled partial image to N blocks of aninput partial image (e.g., using the matching method illustrated in FIG.8) and obtain N first block scores for the first enrolled image as aresult of the matching. In operation 810, the same matching operationdiscussed above with respect to the first enrolled partial image may beperformed for the remaining enrolled partial images from among the Lenrolled partial images. As a result, the fingerprint recognitionapparatus may calculate, for example, N×L block scores, based on aresult of the matching. Descriptions of operations 710 and 720 providedwith reference to FIG. 7 may be applicable to operation 810.

In operation 820, the fingerprint recognition apparatus determines adesired or, alternatively, optimal rotation angle (R) for each enrolledpartial image. The desired or, alternatively, optimal rotation angle foreach enrolled partial image may be an angle used to rotate the blocks inresponse to the enrolled partial images. Further, in operation 820, thefingerprint recognition apparatus may select a highest block score fromamong first N block scores determined for the first enrolled imageduring the matching operation that was performed for the first enrolledimage during operation 810. The fingerprint recognition apparatus mayextract a rotation angle from matching information in which the selectedhighest block score is calculated. For example, in operation 820, thefingerprint recognition apparatus may extract, for the first enrolledpartial image, a rotation angle R. The rotation angle R extracted forthe first enrolled partial image is the rotation angle of the block thatwas determined to have the highest block score during the matching thatwas performed for the first enrolled image during operation 810. Thematching information may be a pair including the rotation angle R and atranslation of the block that was determined to have the highest blockscore during the matching operation that was performed for the firstenrolled image during operation 810. The fingerprint recognitionapparatus may determine the extracted rotation angle R to be the desiredor, alternatively, optimal rotation angle to use for rotating all Nblocks with respect to the first enrolled partial image. In operation820, the fingerprint recognition apparatus may use the same processdescribed above with respect to the first enrolled partial image of theL enrolled partial images to determine a desired or, alternatively,optimal rotation angle R for each of the remaining enrolled partialimages of the L enrolled partial images. Thus, in operation 820, thefingerprint recognition apparatus may determine L optimal rotationangles R for L enrolled images, respectively.

In operation 830, the fingerprint recognition apparatus rotates the Nblocks based on the desired or, alternatively, optimal rotation angle Rdetermined in operation 820 for each of the L enrolled partial images,respectively. For example, in operation 830, the fingerprint recognitionapparatus may rotate the N blocks by the desired or, alternatively,optimal rotation angle R determined in operation 820 for the firstenrolled partial image of the L enrolled partial images. Also, inoperation 830, the fingerprint recognition apparatus may rotate the Nblocks by the desired or, alternatively, optimal rotation angles Rdetermined in operation 820 for the remaining enrolled partial images ofthe L enrolled partial images, respectively. In operation 850, thefingerprint recognition apparatus compares the blocks rotated inresponse to the enrolled partial images to the enrolled partial images.For example, the fingerprint recognition apparatus may re-match theblocks rotated in response to each of the enrolled partial images to acorresponding enrolled partial image using a frequency-based matchingmethod. According to at least some example embodiments, the re-matchingperformed by the fingerprint recognition apparatus in operation 850 mayinclude operations 1060-1080 of FIG. 8, and may exclude operations1011-1050. Here, the fingerprint recognition apparatus may determinetranslations of the blocks while maintaining the rotation angles of theblocks. The fingerprint recognition apparatus may calculate second blockscores based on a result of the re-matching in operation 850. Accordingto at least some example embodiments, the re-matching performed by thefingerprint recognition apparatus in operation 850 may be performed foreach of the L enrolled partial images.

According to at least some example embodiments, operations 820, 830 and850 may be performed with respect to all L enrolled partial images inthe manner discussed above. However, as will be discuss in greaterdetail below, according to at least some example embodiments, anadditional ranking operation may be performed in operation 840, andoperations 820, 830 and 850 may be performed for less than all Lenrolled partial images. For example, the fingerprint recognitionapparatus may not use all the L enrolled partial images to calculate thesecond block scores in operation 850. In operation 840, the fingerprintrecognition apparatus arranges the L enrolled partial images based onthe first block scores (e.g., the N block scores determined for each ofthe L enrolled partial images in operation 810). For example, thefingerprint recognition apparatus may arrange the enrolled partialimages, starting from an enrolled partial image relating to a highestfirst block score. For example, the fingerprint recognition apparatusmay rank the L enrolled partial images from highest to lowest based onfirst block scores determined in operation 810. For example, thefingerprint recognition apparatus may rank the L enrolled partial imagesfrom highest to lowest based on block score averages of the L enrolledpartial images where, for each enrolled partial image from among the Lenrolled images, the block score average of the enrolled partial imageis an average of the N block scores determined for the enrolled partialimage in operation 810. As another example, the fingerprint recognitionapparatus may rank the L enrolled partial images from highest to lowestbased on highest block scores of the L enrolled partial images where,for each enrolled partial image from among the L enrolled images, thehighest block score of the enrolled partial image is the highest blockscore from among of the N block scores determined for the enrolledpartial image in operation 810. The fingerprint recognition apparatusmay select M enrolled partial images based on the arrangement order. Forexample, the fingerprint recognition apparatus may select the M highestranked enrolled partial images from among the L enrolled partial images.Here, M is a positive integer less than L. By comparing only the Menrolled partial images selected from among the L enrolled partialimages to an input partial image, a processing speed of the fingerprintrecognition apparatus may be increased.

In such a case, in operation 820, the fingerprint recognition apparatuscalculates desired or, alternatively, optimal rotation angles for the Menrolled partial images (i.e., not for all L enrolled partial images).In operation 830, the fingerprint recognition apparatus rotates theblocks based on a desired or, alternatively, optimal rotation angle foreach of the M enrolled partial images (i.e., not for all L enrolledpartial images). In operation 850, the fingerprint recognition apparatuscalculates second block scores, for example, N×M block scores (i.e., notfor all L enrolled partial images).

In operation 860, the fingerprint recognition apparatus selects top Kblock scores from among the N×M block scores (or, alternatively, the N×Lblock scores). The fingerprint recognition apparatus calculates afeature value (Val) based on the top K block scores. In operation 870,the fingerprint recognition apparatus determines user authentication bycomparing the feature value to a threshold value. For example, when thefeature value is greater than the threshold value, the fingerprintrecognition apparatus determines the authentication to be successful inoperation 880. Conversely, when the feature value is less than or equalto the threshold value, the fingerprint recognition apparatus determinesthe authentication to be a failure in operation 890.

Although the comparison using a plurality of enrolled images isdescribed in the foregoing, the same authentication method may beapplicable to a case in which a single enrolled image is present, L=1.In such a case, the value of K may be determined in a range of greaterthan or equal to 1 and less than or equal to N. In addition, although aninput image and an enrolled image are described as a partial fingerprintimage, the same authentication method may be applicable to a case inwhich the input and the enrolled images are an entire fingerprint image.

FIG. 12 is a diagram illustrating an example of an electronic system1200 according to at least one example embodiment. Referring to FIG. 12,the electronic system 1200 includes a sensor 920, a processor 910, and amemory 930. The sensor 920, the processor 910, and the memory 930 maycommunicate with one another through a bus 940. The processor 910 maybe, for example, a hardware-implemented data processing device havingcircuitry that is physically structured to execute desired operationsincluding, for example, operations represented as code and/orinstructions included in a program. Examples of the above-referencedhardware-implemented data processing device include, but are not limitedto, a microprocessor, a central processing unit (CPU), a processor core,a multi-core processor; a multiprocessor, an application-specificintegrated circuit (ASIC), and a field programmable gate array (FPGA).Processors executing program code are programmed processors, and thus,are special-purpose computers. According to at least some exampleembodiments, the electronic system 1200 is an example of the fingerprintrecognition apparatus 300 discussed in FIG. 3. For example, anyfunctions described herein as being performed by the fingerprintrecognition apparatus 300 or a fingerprint recognition apparatus may beperformed by the fingerprint recognition apparatus 300 including thestructure of the electronic system 1200. For example, the memory 930 maystore program code including instructions configured to cause theprocessor 910, when executing the program code, to perform any or allfunctions described in the present disclosure as being performed by thefingerprint recognition apparatus 300, a fingerprint recognitionapparatus, and/or the electronic system 1200.

The sensor 920 may be the fingerprint sensor 310 illustrated in FIG. 3.The sensor 920 may capture a fingerprint image using a well-knownmethod, for example, a method of converting a desired or, alternatively,optimal image to an electrical signal. The captured fingerprint imagemay be output to the processor 910.

The processor 910 may include at least one device or unit described withreference to FIGS. 1 through 11, or perform at least one methoddescribed with reference to FIGS. 1 through 11. For example, theprocessor 910 may include the fingerprint recognition apparatus 300 ofFIG. 3. The memory 930 may store partial images captured by the sensor920 and then enrolled, an input partial image captured by the sensor920, a result of matching processed by the processor 910, and/or blockscores calculated by the processor 910. The memory 930 may be a volatilememory or a nonvolatile memory.

The processor 910 may execute a program and control the electronicsystem 1200. A program code executed by the processor 910 may be storedin the memory 930. The electronic system 1200 may be connected to anexternal device, for example, a PC and a network, through an input andoutput device (not shown), and may exchange data with the externaldevice.

The electronic system 1200 may be provided in various forms, forexample, a mobile device such as a mobile phone, a smartphone, apersonal digital assistant (PDA), a tablet computer, and a laptopcomputer, a computing device such as a PC, a tablet computer, and anetbook, and a television (TV), a smart TV, and a security device forgate control.

Although examples described herein relate to recognizing a user using aportion of a fingerprint of the user, such examples may be furtherapplied to recognizing the user using a portion of biodata of the user.The biodata may include information about the fingerprint, bloodvessels, and an iris of the user. In such an application, the processor910 may receive input partial data corresponding to the portion of thebiodata of the user, partition the input partial data into blocks,compare the blocks to enrolled partial data sets corresponding topartial data sets of enrolled biodata, and recognize the user based on aresult of the comparing.

In an example, the sensor 920 may include a sensor configured torecognize a blood vessel pattern of the user. The sensor 920 may extractthe blood vessel pattern from a dorsal hand skin of the user. The sensor920 may increase or, alternatively, maximize a brightness of bloodvessels against a brightness of the skin using an infrared lighting andfilter, and obtain an image including the blood vessel pattern. In suchan example, the processor 910 may recognize the user by comparing apartial image corresponding to a portion of the blood vessel pattern toa partial image corresponding to an enrolled blood vessel pattern.

In another example, the sensor 920 may include a sensor configured torecognize an iris pattern of the user. The sensor 920 may scan orcapture the iris pattern between a pupil and a sclera, which is a whitearea of an eye, of the user. The sensor 920 may obtain a partial imagecorresponding to a portion of the iris pattern. In such an example, theprocessor 910 may recognize the user by comparing the partial imagecorresponding to the portion of the iris pattern to a partial imagecorresponding to an enrolled iris pattern.

The units and/or modules described herein may be implemented usinghardware components and software components. For example, the hardwarecomponents may include microphones, amplifiers, band-pass filters, audioto digital convertors, and processing devices. A processing device maybe implemented using one or more hardware device configured to carry outand/or execute program code by performing arithmetical, logical, andinput/output operations. The processing device(s) may include aprocessor, a controller and an arithmetic logic unit, a digital signalprocessor, a microcomputer, a field programmable array, a programmablelogic unit, a microprocessor or any other device capable of respondingto and executing instructions in a defined manner. The processing devicemay run an operating system (OS) and one or more software applicationsthat run on the OS. The processing device also may access, store,manipulate, process, and create data in response to execution of thesoftware. For purpose of simplicity, the description of a processingdevice is used as singular; however, one skilled in the art willappreciated that a processing device may include multiple processingelements and multiple types of processing elements. For example, aprocessing device may include multiple processors or a processor and acontroller. In addition, different processing configurations arepossible, such a parallel processors.

The software may include a computer program, a piece of code, aninstruction, or some combination thereof, to independently orcollectively instruct and/or configure the processing device to operateas desired, thereby transforming the processing device into a specialpurpose processor. Software and data may be embodied permanently ortemporarily in any type of machine, component, physical or virtualequipment, computer storage medium or device, or in a propagated signalwave capable of providing instructions or data to or being interpretedby the processing device. The software also may be distributed overnetwork coupled computer systems so that the software is stored andexecuted in a distributed fashion. The software and data may be storedby one or more non-transitory computer readable recording mediums.

The methods according to the above-described example embodiments may berecorded in non-transitory computer-readable media including programinstructions to implement various operations of the above-describedexample embodiments. The media may also include, alone or in combinationwith the program instructions, data files, data structures, and thelike. The program instructions recorded on the media may be, forexample, those specially designed and constructed for the purposes ofexample embodiments. Examples of non-transitory computer-readable mediainclude magnetic media such as hard disks, floppy disks, and magnetictape; optical media such as CD-ROM discs, DVDs, and/or Blue-ray discs;magneto-optical media such as optical discs; and hardware devices thatare specially configured to store and perform program instructions, suchas read-only memory (ROM), random access memory (RAM), flash memory(e.g., USB flash drives, memory cards, memory sticks, etc.), and thelike. Examples of program instructions include both machine code, suchas produced by a compiler, and files containing higher level code thatmay be executed by the computer using an interpreter. Theabove-described devices may be configured to act as one or more softwaremodules in order to perform the operations of the above-describedexample embodiments, or vice versa.

It will be apparent to those skilled in the art that variousmodifications and variations can be made to the example embodimentswithout departing from the spirit or scope of the inventive conceptsdescribed herein. Thus, it is intended that the example embodimentscover the modifications and variations of the example embodimentsprovided they come within the scope of the appended claims and theirequivalents.

1. A bioimage recognition method, comprising: receiving an input partialimage corresponding to a partial image of a bioimage of a user;partitioning the input partial image into a plurality of blocks;performing a comparison operation based on the plurality of blocks andenrolled partial images corresponding to partial images of an enrolledbioimage; and recognizing the bioimage of the user based on a result ofthe comparison operation, wherein the performing the comparisonoperation comprises: calculating scores by matching the plurality ofblocks to the enrolled partial images, and selecting first scores fromamong the calculated scores, a total number of the first scores beingless than a total number of the calculated scores, and wherein therecognizing comprises: recognizing the bioimage of the user based on theselected first scores.
 2. The method of claim 1, wherein the receivingcomprises: sensing a partial region of the bioimage of the user througha sensing region smaller than a size of the bioimage of the user.
 3. Themethod of claim 1, further comprising: generating the enrolled partialimages by iteratively sensing partial regions of a bioimage of anenrolled user through a sensing region smaller than a size of thebioimage of the enrolled user.
 4. The method of claim 1, wherein thepartitioning comprises: partitioning the input partial image into theplurality of blocks based on a first pattern.
 5. The method of claim 1,wherein the performing the comparison operation comprises: comparing afirst block among the plurality of blocks to the enrolled partialimages; and comparing a second block among the plurality of blocks tothe enrolled partial images.
 6. The method of claim 1, wherein theperforming the comparison operation comprises: calculating scoresindicating a degree of matching between the plurality of blocks and theenrolled partial images.
 7. The method of claim 1, wherein theperforming the comparison operation comprises: matching the plurality ofblocks to the enrolled partial images; and comparing the plurality ofblocks to the enrolled partial images based on a result of the matching.8. The method of claim 1, wherein the performing the comparisonoperation comprises: determining first rotation angles with respect tothe enrolled partial images by matching the plurality of blocks to theenrolled partial images; rotating the plurality of blocks based on thefirst rotation angles with respect to the enrolled partial images; andcomparing the plurality of blocks rotated based on the first rotationangles to the enrolled partial images.
 9. The method of claim 1, whereinthe performing the comparison operation comprises: calculating scores bymatching the plurality of blocks to the enrolled partial images;selecting a first images from among the enrolled partial images based onthe calculated scores, a total number of the first images being lessthan a total number of the enrolled partial images; determining firstrotation angles with respect to the first images based on the calculatedscores; rotating the plurality of blocks based on the first rotationangles with respect to the first images; and comparing the plurality ofblocks rotated based on the first rotation angles to the first images.10. The method of claim 1, wherein the recognizing comprises at leastone of: authenticating the user based on the result of the comparisonoperation; and identifying the user based on the result of thecomparison operation.
 11. The method of claim 1, wherein the recognizingcomprises: selecting a first number of pairs of the plurality of blocksand the enrolled partial images based on the result of the comparisonoperation; and recognizing the bioimage of the user based on theselected pairs.
 12. The method of claim 1, wherein the bioimage includesat least one of information corresponding to a fingerprint image,information corresponding to a blood vessel image, or informationcorresponding to an iris image.
 13. A computer-readable medium storing acomputer program that includes instructions which, when executed by oneor more processors, cause the one or more processors to perform themethod of claim
 1. 14. A bioimage recognition apparatus, comprising: asensor configured to receive an input image corresponding to a bioimageof a user; and at least one processor configured to partition the inputimage into a plurality of blocks, perform a comparison operation basedon the plurality of blocks and at least one enrolled image correspondingto an enrolled bioimage, and recognize the bioimage of the user based ona result of the comparison operation, wherein the at least one processoris configured to, calculate scores by matching the plurality of blocksto the at least one enrolled image, select first scores from among thecalculated scores, a total number of the first scores being less than atotal number of the calculated scores, and recognize the bioimage of theuser based on the selected first scores.
 15. The apparatus of claim 14,wherein the input image corresponds to a partial image of the bioimageof the user, and the at least one enrolled image corresponds to at leastone partial image of the enrolled bioimage.
 16. The apparatus of claim14, wherein the at least one processor is configured to match theplurality of blocks to the at least one enrolled image, and calculatescores indicating a degree of matching between the plurality of blocksand the at least one enrolled image.
 17. The apparatus of claim 14,wherein the at least one processor is configured to, determine a firstrotation angle of the at least one enrolled image by matching theplurality of blocks to the at least one enrolled image, rotate theplurality of blocks based on the determined first rotation angle, andcompare, to the at least one enrolled image, the plurality of blocksrotated based on the determined first rotation angle.
 18. The apparatusof claim 14, wherein the at least one processor is configured to,calculate scores by matching the plurality of blocks to the at least oneenrolled image, select a first number of enrolled images from among theat least one enrolled image based on the calculated scores, determinefirst rotation angles with respect to the selected enrolled images basedon the calculated scores, rotate the plurality of blocks based on thefirst rotation angles with respect to the selected enrolled images, andcompare, to the selected enrolled images, the plurality of blocksrotated based on the first rotation angles, wherein the at least oneenrolled image is a plurality of enrolled images, and wherein the firstnumber is less than a total number of the plurality of enrolled images.19. The apparatus of claim 14, wherein the at least one processor isconfigured to authenticate the user based on the result of thecomparison operation or identify the user based on the result of thecomparison operation.
 20. The apparatus of claim 14, wherein the atleast one processor is configured to, select a first number of pairs ofthe plurality of blocks and the at least one enrolled image based on theresult of the comparison operation, and recognize the bioimage of theuser based on the selected pairs.